Results 61 to 70 of about 1,715,429 (285)

Features for unsupervised document classification [PDF]

proceeding of the 6th conference on Natural language learning - COLING-02, 2002
Unsupervised document classification is an important problem in practical text mining since training data is seldom available. In this paper we study the problem of term selection and the performance of various features for unsupervised text classification.
openaire   +2 more sources

Bi‐ and Mono‐Allelic RFC1 Expansion in a North American Cohort With Idiopathic Axonal Neuropathy

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective RFC1 biallelic repeat expansion is increasingly recognized as a cause of chronic idiopathic axonal polyneuropathy (CIAP), but it remains challenging to know who to test. This study aims to determine the prevalence of biallelic and monoallelic RFC1 expansions and their corresponding neuropathy phenotypes in CIAP patients and identify ...
Amro M. Stino, Lavanya Muthukumar, Evan L. Reynolds, Peter Todd, Sinem Ovunc, Sheng Chih Jin, Zitian Tang, Simone Thomas, PNRR Study Group, Ahmet Höke, Brian C. Callaghan, Sarah Berth, Vinay Chaudhry, David Cornblath, Leana Doherty, Lindsey Hayes, Hristelina Ilieva, Thomas Lloyd, Mohammad Khoshnoodi, Brett McCray, Brett Morrison, Bipasha Mukherjee‐Clavin, Lyle Ostrow, Michael Polydefkis, Ricardo Roda, Charlotte Sumner   +25 more
wiley   +1 more source

Cloud Classification with Unsupervised Deep Learning

CoRR, 2022
5 pages, 6 figures, Proceedings for Climate Informatics Workshop 2019 ...
Takuya Kurihana, Ian T. Foster, Rebecca Willett, Sydney Jenkins, Kathryn Koenig, Ruby Werman, Ricardo Barros Lourenço, Casper Neo, Elisabeth Moyer   +8 more
openaire   +2 more sources

Integrated PANoptosis Profiling Identifies Immunosuppressive Subtypes and a Prognostic Signature With Functional Validation of MLKL in Glioblastoma

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective The prognosis of glioblastoma (GBM) remains highly unfavorable, largely due to high tumor heterogeneity and an immunosuppressive microenvironment. However, the functional role of PANoptosis in this context is poorly understood. Methods Patients were stratified via K‐means clustering. A risk score model was constructed using prognosis‐
Langfei Tian, Minghui Zhao, Yuanbo Hu, Kaiyue Wang, Haiguang Liu, Zetong Bai, Kebin Zheng   +6 more
wiley   +1 more source

Unsupervised classification of noisy chromosomes [PDF]

Bioinformatics, 2001
Abstract Motivation: Almost all methods of chromosome recognition assume supervised training; i.e. we are given correctly classified chromosomes to start the training phase. Noise, if any, is confined only in the representation of the chromosomes and not in the classification of the chromosomes.
openaire   +2 more sources

Congested scene classification via efficient unsupervised feature learning and density estimation

, 2016
An unsupervised learning algorithm with density information considered is proposed for congested scene classification. Though many works have been proposed to address general scene classification during the past years, congested scene classification is ...
Yuan, Yuan, Wang, Qi, Wan, Jia
core   +1 more source

Data‐Driven SuStaIn Model of Disability Progression in Amyotrophic Lateral Sclerosis

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To determine whether ordinal Subtype and Stage Inference (SuStaIn) applied to routine ALSFRS‐R item scores can identify reproducible disability progression patterns in amyotrophic lateral sclerosis (ALS) and provide clinically meaningful staging.
Giammarco Milella, Veria Vacchiano, Vittorio Velucci, Stefano Zoccolella, Rocco Liguori, Giovanni Defazio   +5 more
wiley   +1 more source

The Spatio-Temporal Equalization Sliding-Window Distribution Distance Maximization Based on Unsupervised Learning for Online Event-Related Potential-Based Brain–Computer Interfaces

Machines
Brain–computer interfaces (BCIs) provide a direct communication pathway between the central nervous system and external environments, enabling human–machine interaction control.
Haoye Wang, Jing Jin, Xinjie He, Shurui Li, Andrzej Cichocki   +4 more
doaj   +1 more source

Unsupervised Feature Selection Based on Ultrametricity and Sparse Training Data: A Case Study for the Classification of High-Dimensional Hyperspectral Data

Remote Sensing, 2018
In this paper, we investigate the potential of unsupervised feature selection techniques for classification tasks, where only sparse training data are available.
Patrick Erik Bradley, Sina Keller, Martin Weinmann   +2 more
doaj   +1 more source

An Unsupervised Sentiment Classification Method Based on Multi-Level Fuzzy Computing and Multi-Criteria Fusion

IEEE Access, 2020
With the rapid growth of user-generated content, unsupervised methods that do not require label training data have gradually become a research focus in the field of sentiment classification and natural language processing.
Bingkun Wang, Weina He, Zhen Yang, Shufeng Xiong   +3 more
doaj   +1 more source